The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Neural Computation
Neural Networks - Special issue on organisation of computation in brain-like systems
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Sequence Learning: From Recognition and Prediction to Sequential Decision Making
IEEE Intelligent Systems
On Intelligence
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Why Can't a Computer be more Like a Brain?
IEEE Spectrum
Incremental learning of complex temporal patterns
IEEE Transactions on Neural Networks
Context in temporal sequence processing: a self-organizing approach and its application to robotics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Anticipation-Based Temporal Sequences Learning in Hierarchical Structure
IEEE Transactions on Neural Networks
A question answer approach to building semantic memory
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Robotics and Autonomous Systems
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Design of artificial neural structures capable of reliable and flexible long-term spatio-temporal memory is of paramount importance in machine intelligence. To this end, we propose a novel, biologically inspired, long-term memory (LTM) architecture. We intend to use it as a building block of a neuron-level architecture that is able to mimic natural intelligence through learning, anticipation, and goal-driven behavior. A mutual input enhancement and blocking structure is proposed, and its operation is discussed in detail. The paper focuses on a hierarchical memory organization, storage, recognition, and recall mechanisms. Simulation results of the proposed memory show its effectiveness, adaptability, and robustness. Accuracy of the proposed method is compared to other methods including Levenshtein distance method and a Markov chain.